Running principal component and k-means clustering analyses
dir.create("./results", showWarnings = F)
dir.create("./results/figures", showWarnings = F)
source("./src/GM_stats.R")
Performing GPA
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Making projections... Finished!



[1] -0.9999859
[1] 0.9999956
[1] -0.9999535
[1] -0.9999967
[1] -0.999442
[1] -0.9999388
[1] -1
[1] 0.9997545
[1] -0.9996835
[1] 0.9999969

Plotting PC and k-means results
K-means clustering plot:

Kennel-club groupings and PC results:
Too few points to calculate an ellipse

Discriminant Factors between groups
Comparison of landmarks between group means
# Extracting first 5 PCs and each kennel-club grouping scheme
skulls <- cbind(SlicerMorph.repc[, 1:5],
"UKC" = factor(SlicerMorph.repc$UKC, ordered = T, levels = UKC_group_order),
"AKC" = factor(SlicerMorph.repc$AKC, ordered = T, levels = AKC_group_order),
"bitework" = factor(SlicerMorph.repc$Bitework),
"scentwork" = factor(SlicerMorph.repc$Nosework))
skulls <- na.omit(skulls)
# Create a paired and ordered GPA-grouping object
mixdrop<-gpa$coords[,,order(dimnames(gpa$coords)[[3]])]
mixdrop<-mixdrop[,,-96]
reforge<-geomorph.data.frame(shape = mixdrop,
UKC = na.omit(factor(SlicerMorph.repc$UKC, ordered = T)),
AKC = na.omit(factor(SlicerMorph.repc$AKC, ordered = T)),
bitework = na.omit(factor(SlicerMorph.repc$Bitework[-96])),
scentwork = na.omit(factor(SlicerMorph.repc$Nosework[-96])))
# Create a function that subsets the mean landmarks for each group and does a pairwise subtraction and squaring of those means
meanforge <- function(target, group) {
#sub-setting the means and sending them to a new object
new.coords<-coords.subset(target[["shape"]], group = target[[group]])
output_means<-lapply(new.coords, mshape)
#perform the subtraction and squaring of the pairwise combinations
result<- combn(output_means, 2, function(x) (x[[1]]-x[[2]])^2, simplify = FALSE)
#rename the pairs so they're readable
names(result)<-combn(names(output_means), 2, function(n) paste(n[1], "-", n[2]), simplify = TRUE)
return(result)
}
#now run the function for each grouping scheme. Each member of the list will be named based on which two groups are compared
meanshape.UKC<-meanforge(reforge,"UKC")
meanshape.AKC<-meanforge(reforge,"AKC")
meanshape.nose<-meanforge(reforge,"scentwork")
meanshape.bite<-meanforge(reforge,"bitework")
Reformatting data for visualization:
calc_gpa_dist <- function(dat){
require(tidyr)
n <- length(names(dat))
results <- matrix(NA, nrow = nrow(dat[[1]]), ncol = n)
results <- as.data.frame(results)
results <- cbind(seq(1,nrow(results)), results)
colnames(results) <- c("landmark",names(dat))
for(i in names(dat)){
results[[i]] <- sqrt(dat[[i]][,"X"]^2 + dat[[i]][,"Y"]^2 + dat[[i]][,"Z"]^2)
}
results_long <- pivot_longer(results, cols = -"landmark", names_to = "comparison")
results_long$landmark <- factor(results_long$landmark)
return(results_long)
}
meanshape.AKC[["plotting"]] <- calc_gpa_dist(meanshape.AKC)
meanshape.UKC[["plotting"]] <- calc_gpa_dist(meanshape.UKC)
meanshape.bite[["plotting"]] <- calc_gpa_dist(meanshape.bite)
meanshape.bite[["cutoff"]] <- data.frame("mean" = mean(meanshape.bite[["plotting"]]$value),
"sd" = sd(meanshape.bite[["plotting"]]$value))
meanshape.nose[["plotting"]] <- calc_gpa_dist(meanshape.nose)
meanshape.nose[["cutoff"]] <- data.frame("mean" = mean(meanshape.nose[["plotting"]]$value),
"sd" = sd(meanshape.nose[["plotting"]]$value))
meanshape.AKC[["plotting"]]$short <- ifelse(grepl("toy", meanshape.AKC[["plotting"]]$comparison,
ignore.case = T),
"toy",
ifelse(grepl("natural", meanshape.AKC[["plotting"]]$comparison,
ignore.case = T),
"natural", "other"))
meanshape.AKC[["plotting"]]$short <- factor(meanshape.AKC[["plotting"]]$short,
ordered = T, levels = c("toy", "natural", "other"))
meanshape.AKC[["cutoff"]] <- data.frame("mean" = mean(meanshape.AKC[["plotting"]]$value),
"sd" = sd(meanshape.AKC[["plotting"]]$value))
meanshape.UKC[["plotting"]]$short <- ifelse(grepl("companion", meanshape.UKC[["plotting"]]$comparison,
ignore.case = T),
"companion",
ifelse(grepl("natural", meanshape.UKC[["plotting"]]$comparison,
ignore.case = T),
"natural", "other"))
meanshape.UKC[["plotting"]]$short <- factor(meanshape.UKC[["plotting"]]$short,
ordered = T, levels = c("companion", "natural", "other"))
meanshape.UKC[["cutoff"]] <- data.frame("mean" = mean(meanshape.UKC[["plotting"]]$value),
"sd" = sd(meanshape.UKC[["plotting"]]$value))
AKC & UKC groups:
p_UKC_compare <- ggplot(meanshape.UKC[["plotting"]], aes(landmark, value, color = short)) +
geom_point(position = position_jitter(width = 0.1)) +
geom_hline(yintercept = meanshape.UKC[["cutoff"]]$mean+2*meanshape.UKC[["cutoff"]]$sd,
lty = 2) +
scale_color_viridis_d(name = "Comparison\nagainst:") +
annotate("segment", x = 1, xend = 20, y = 0.0085, yend = 0.0085,
lty = 1, color = "red") +
annotate("text", x = 10, y = 0.0075, label = "Dorsal features", color = "red") +
annotate("segment", x = 21, xend = 38, y = 0.0085, yend = 0.0085,
lty = 1, color = "blue") +
annotate("text", x = 30, y = 0.0075, label = "Ventral features", color = "blue") +
ylab("Group-mean landmark\ndistance squared") +
ggtitle("UKC group comparisons") +
theme_bw() #+
#theme(legend.position = "bottom")
#theme(legend.position = "inside", legend.position.inside = c(0.75,0.65),
# legend.background = element_rect(color = "gray30", fill = "white", linetype="solid",
# linewidth = 0.2))
p_AKC_compare <- ggplot(meanshape.AKC[["plotting"]], aes(landmark, value, color = short)) +
geom_point(position = position_jitter(width = 0.1)) +
geom_hline(yintercept = meanshape.AKC[["cutoff"]]$mean+2*meanshape.AKC[["cutoff"]]$sd,
lty = 2) +
scale_color_viridis_d(name = "Comparison\nagainst:") +
annotate("segment", x = 1, xend = 20, y = 0.0085, yend = 0.0085,
lty = 1, color = "red") +
annotate("text", x = 10, y = 0.0075, label = "Dorsal features", color = "red") +
annotate("segment", x = 21, xend = 38, y = 0.0085, yend = 0.0085,
lty = 1, color = "blue") +
annotate("text", x = 30, y = 0.0075, label = "Ventral features", color = "blue") +
ylab("Group-mean landmark\ndistance squared") +
ggtitle("AKC group comparisons") +
theme_bw() #+
#theme(legend.position = "bottom")
#theme(legend.position = "inside", legend.position.inside = c(0.75,0.65),
# legend.background = element_rect(color = "gray30", fill = "white", linetype="solid",
# linewidth = 0.2))
p_AKC_compare / p_UKC_compare

Nosework and Bitework:
p_bite_compare <- ggplot(meanshape.bite[["plotting"]], aes(landmark, value, color = comparison)) +
geom_point(position = position_jitter(width = 0.1)) +
geom_hline(yintercept = meanshape.bite[["cutoff"]]$mean+2*meanshape.bite[["cutoff"]]$sd,
lty = 2) +
annotate("segment", x = 1, xend = 20, y = 0.007, yend = 0.007,
lty = 1, color = "red") +
annotate("text", x = 10, y = 0.0065, label = "Dorsal features", color = "red") +
annotate("segment", x = 21, xend = 38, y = 0.007, yend = 0.007,
lty = 1, color = "blue") +
annotate("text", x = 30, y = 0.0065, label = "Ventral features", color = "blue") +
scale_color_viridis_d(name = "Group-group\ncomparison") +
ylab("Group-mean landmark\ndistance squared") +
ggtitle("Bite-work group comparisons") +
theme_bw()
p_nose_compare <- ggplot(meanshape.nose[["plotting"]], aes(landmark, value, color = comparison)) +
geom_point(position = position_jitter(width = 0.1)) +
geom_hline(yintercept = meanshape.nose[["cutoff"]]$mean+2*meanshape.nose[["cutoff"]]$sd,
lty = 2) +
annotate("segment", x = 1, xend = 20, y = 0.007, yend = 0.007,
lty = 1, color = "red") +
annotate("text", x = 10, y = 0.0065, label = "Dorsal features", color = "red") +
annotate("segment", x = 21, xend = 38, y = 0.007, yend = 0.007,
lty = 1, color = "blue") +
annotate("text", x = 30, y = 0.0065, label = "Ventral features", color = "blue") +
scale_color_viridis_d(name = "Group-group\ncomparison") +
ylab("Group-mean landmark\ndistance squared") +
ggtitle("Scent-work group comparisons") +
theme_bw()
p_bite_compare / p_nose_compare + plot_layout(guides = "collect")

MANOVA
man_UKC <- manova(as.matrix(skulls[,1:5])~skulls$UKC)
summary.aov(man_UKC, test="Pillai")
Response PC.1 :
Df Sum Sq Mean Sq F value Pr(>F)
skulls$UKC 8 0.65020 0.081275 35.372 < 2.2e-16 ***
Residuals 107 0.24585 0.002298
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Response PC.2 :
Df Sum Sq Mean Sq F value Pr(>F)
skulls$UKC 8 0.073427 0.0091783 6.0866 1.902e-06 ***
Residuals 107 0.161352 0.0015080
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Response PC.3 :
Df Sum Sq Mean Sq F value Pr(>F)
skulls$UKC 8 0.016265 0.00203313 2.4906 0.01612 *
Residuals 107 0.087348 0.00081633
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Response PC.4 :
Df Sum Sq Mean Sq F value Pr(>F)
skulls$UKC 8 0.024974 0.00312178 5.7231 4.625e-06 ***
Residuals 107 0.058365 0.00054547
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Response PC.5 :
Df Sum Sq Mean Sq F value Pr(>F)
skulls$UKC 8 0.000574 0.00007179 0.1633 0.9951
Residuals 107 0.047029 0.00043952
#using Geomorph and doing pairwise comparisons
manUKC<-procD.lm(shape ~ UKC, data = reforge, RRPP = TRUE)
manAKC<-procD.lm(shape ~ AKC, data = reforge, RRPP = TRUE)
manBite<-procD.lm(shape ~ bitework, data = reforge, RRPP = TRUE)
manScent<-procD.lm(shape ~ scentwork, data = reforge, RRPP = TRUE)
hold.ukc<-manova(manUKC)
hold.akc<-manova(manAKC)
hold.bite<-manova(manBite)
hold.nose<-manova(manScent)
ukcpairs<-pairwise(manUKC, groups = reforge$UKC)
akcpairs<-pairwise(manAKC, groups = reforge$AKC)
bitepairs<-pairwise(manBite, groups = reforge$bitework)
nosepairs<-pairwise(manScent, groups = reforge$scentwork)
pvals.UKC<-summary(ukcpairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.UKC<-pvals.UKC$pairwise.tables$P[lower.tri(pvals.UKC$pairwise.tables$P)]
pvals.AKC<-summary(akcpairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.AKC<-pvals.AKC$pairwise.tables$P[lower.tri(pvals.AKC$pairwise.tables$P)]
pvals.bite<-summary(bitepairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.bite<-pvals.bite$pairwise.tables$P[lower.tri(pvals.bite$pairwise.tables$P)]
pvals.nose<-summary(nosepairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.nose<-pvals.nose$pairwise.tables$P[lower.tri(pvals.nose$pairwise.tables$P)]
pvals.ukc.adjusted<-p.adjust(pvals.UKC, method = "bonf")
pvals.akc.adjusted<-p.adjust(pvals.AKC, method = "bonf")
pvals.bite.adjusted<-p.adjust(pvals.bite, method = "bonf")
pvals.nose.adjusted<-p.adjust(pvals.nose, method = "bonf")
lda_UKC <- MASS::lda(skulls$UKC~as.matrix(skulls[,1:5]))
lda_UKC
Call:
lda(skulls$UKC ~ as.matrix(skulls[, 1:5]))
Prior probabilities of groups:
NATURAL Herding Terrier Companion Gun dog Guardian Northern Breed
0.19827586 0.23275862 0.08620690 0.12068966 0.05172414 0.09482759 0.02586207
Sighthound Scenthound
0.14655172 0.04310345
Group means:
as.matrix(skulls[, 1:5])PC.1 as.matrix(skulls[, 1:5])PC.2 as.matrix(skulls[, 1:5])PC.3
NATURAL -0.11869015 -0.019063433 0.04817564
Herding -0.12101159 0.026436494 0.04577986
Terrier -0.05852371 0.001880539 0.04485686
Companion 0.09983210 0.018101105 0.02331451
Gun dog -0.11168214 0.041234328 0.04624008
Guardian -0.06180271 0.065382213 0.06595086
Northern Breed -0.06497801 0.049661060 0.06220728
Sighthound -0.15480497 0.029842740 0.02964807
Scenthound -0.12915491 0.049368307 0.04321946
as.matrix(skulls[, 1:5])PC.4 as.matrix(skulls[, 1:5])PC.5
NATURAL -0.016530397 0.1158473
Herding 0.013615782 0.1190599
Terrier 0.032376419 0.1152870
Companion 0.011348484 0.1182048
Gun dog 0.007520818 0.1204955
Guardian 0.003014570 0.1207284
Northern Breed -0.017512289 0.1170088
Sighthound 0.018704155 0.1147247
Scenthound 0.010813419 0.1131374
Coefficients of linear discriminants:
LD1 LD2 LD3 LD4 LD5
as.matrix(skulls[, 1:5])PC.1 21.465233 2.072852 2.157478 1.232714 0.61490553
as.matrix(skulls[, 1:5])PC.2 2.130815 -20.151953 14.104686 -8.508683 1.48405135
as.matrix(skulls[, 1:5])PC.3 -13.131521 1.216403 21.217452 26.189753 4.17032936
as.matrix(skulls[, 1:5])PC.4 10.561161 -31.562092 -23.132983 17.258716 -0.02015756
as.matrix(skulls[, 1:5])PC.5 2.636558 -2.368104 6.558913 4.347214 -46.98480500
Proportion of trace:
LD1 LD2 LD3 LD4 LD5
0.7331 0.1664 0.0818 0.0172 0.0015
Testing of Functional Groups


bite_yes_canine <- bite_data$canine[bite_data$Bitework == "yes"]
bite_no_canine <- bite_data$canine[bite_data$Bitework == "no"]
shapiro.test(bite_yes_canine)
Shapiro-Wilk normality test
data: bite_yes_canine
W = 0.95076, p-value = 0.5726
shapiro.test(bite_no_canine)
Shapiro-Wilk normality test
data: bite_no_canine
W = 0.97244, p-value = 0.08128
t.test(bite_yes_canine, bite_no_canine)
Welch Two Sample t-test
data: bite_yes_canine and bite_no_canine
t = -0.30306, df = 36.495, p-value = 0.7636
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-9.152725 6.771939
sample estimates:
mean of x mean of y
57.78286 58.97325
bite_yes_carnassial <- bite_data$carnassial[bite_data$Bitework == "yes"]
bite_no_carnassial <- bite_data$carnassial[bite_data$Bitework == "no"]
shapiro.test(bite_yes_carnassial)
Shapiro-Wilk normality test
data: bite_yes_carnassial
W = 0.92608, p-value = 0.2687
shapiro.test(bite_no_carnassial)
Shapiro-Wilk normality test
data: bite_no_carnassial
W = 0.96684, p-value = 0.03577
t.test(bite_yes_carnassial, bite_no_carnassial)
Welch Two Sample t-test
data: bite_yes_carnassial and bite_no_carnassial
t = -0.52911, df = 42.805, p-value = 0.5995
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-9.417946 5.503589
sample estimates:
mean of x mean of y
57.12857 59.08575
bite_natural_canine <- bite_data$canine[bite_data$Bitework == "NATURAL"]
shapiro.test(bite_natural_canine)
Shapiro-Wilk normality test
data: bite_natural_canine
W = 0.91337, p-value = 0.2672
bite_fox_canine <- bite_data$canine[bite_data$Bitework == "FOX"]
shapiro.test(bite_fox_canine)
Shapiro-Wilk normality test
data: bite_fox_canine
W = 0.82835, p-value = 0.03197
t.test(bite_yes_canine, bite_natural_canine)
Welch Two Sample t-test
data: bite_yes_canine and bite_natural_canine
t = -1.6371, df = 13.462, p-value = 0.1248
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-29.241240 3.979681
sample estimates:
mean of x mean of y
57.78286 70.41364
t.test(bite_yes_canine, bite_fox_canine)
Welch Two Sample t-test
data: bite_yes_canine and bite_fox_canine
t = -0.85342, df = 10.859, p-value = 0.4119
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-29.77613 13.15585
sample estimates:
mean of x mean of y
57.78286 66.09300
For the paper:
Figure 2: k-means cluster on PC plot

Figure 3: UKC comparison figure
Too few points to calculate an ellipse

Too few points to calculate an ellipse
Figure 4: AKC comparison figure

Figure 5: task-specific group results

---
title: "Geomorphic Morphometrics on canid skulls"
output: html_notebook
---

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
knitr::opts_chunk$set(echo = TRUE, fig.width = 8.5)
options(knitr.graphics.error = FALSE)
options(knitr.kable.NA = '')
options(rgl.useNULL=TRUE) # Note: OpenGL is depreciated on Macs and requires XQuartz in order to work properly. This will cause an error when loading the Morpho and geomorph packages. Setting this option removes the error. If you have XQuartz and don't mind it opening, suppress this line of code to regain full functionality.

library(tidyverse)
library(janitor)
library(gtools)
library(ggplot2)
library(patchwork)
library(kableExtra)
library(ggrepel)
library(geomorph)
# devtools::install_github('SlicerMorph/SlicerMorphR')
library(SlicerMorphR)
# devtools::install_github("lindsaywaldrop/munchcolors")
library(munchcolors)
```

## Running principal component and k-means clustering analyses

```{r}
dir.create("./results", showWarnings = F)
dir.create("./results/figures", showWarnings = F)
source("./src/GM_stats.R")
```

## Plotting PC and k-means results


```{r plotting-setup, include=FALSE}
# Setting UKC group order
UKC_group_order <- c("NATURAL", "Herding", "Terrier", "Companion", "Gun dog", 
                     "Guardian", "Northern Breed", "Sighthound", "Scenthound")
AKC_group_order <- c("NATURAL", "Herding", "Terrier", "Toy", "Sporting", 
                     "Working", "Non-sporting", "Hound")

# grouping plots ----------------------------------------------------------
# First added your ordered classifiers onto the PCA data
SlicerMorph.repc$UKC <- clsf_reord$UKC.breeding.Standard
SlicerMorph.repc$AKC <- clsf_reord$AKC.breeding.standard
# Set mixed breed to NA for plotting, there is only one!
SlicerMorph.repc$UKC[SlicerMorph.repc$UKC == "MIXED"] <- NA
SlicerMorph.repc$AKC[SlicerMorph.repc$AKC == "MIXED"] <- NA
# Setting fox to natural for AKC/UKC grouping to simplify plots
SlicerMorph.repc$UKC[SlicerMorph.repc$UKC == "FOX"] <- "NATURAL"
SlicerMorph.repc$AKC[SlicerMorph.repc$AKC == "FOX"] <- "NATURAL"

# Changing Yes to lowercase
SlicerMorph.repc$Nosework <- ifelse(clsf_reord$Nose =="Yes" | clsf_reord$Nose == "no", 
                                    tolower(clsf_reord$Nose), clsf_reord$Nose)
# Changing Nosework to ordered factor
SlicerMorph.repc$Nosework <- factor(SlicerMorph.repc$Nosework, ordered = T, 
                                    levels = c("yes", "no", "NATURAL", "FOX"))
# Changing Yes to lowercase 
SlicerMorph.repc$Bitework <- ifelse(clsf_reord$Bitework =="Yes" | clsf_reord$Bitework == "no",
                                    tolower(clsf_reord$Bitework), clsf_reord$Bitework)
# Changing Bitework to ordered factor
SlicerMorph.repc$Bitework <- factor(SlicerMorph.repc$Bitework, ordered = T, 
                                    levels = c("yes", "no", "NATURAL", "FOX"))
# Putting cluster levels into a column as a factor
SlicerMorph.repc$cluster <- factor(km$cluster)

# Now to ease viewing we'll factor them into broad categories

# Turning UKC isnto an ordered factor column
SlicerMorph.repc$UKC.shapefact <- factor(SlicerMorph.repc$UKC, ordered = T, 
                                         levels = UKC_group_order)

## AKC next
SlicerMorph.repc$AKC.shapefact <- factor(SlicerMorph.repc$AKC, 
                                         levels = AKC_group_order)

SlicerMorph.repc$AKC.fact <- factor(SlicerMorph.repc$AKC)

## Setting up a column that is domestic (dogs) and natural (foxes, others)
SlicerMorph.repc$domestic <- factor(ifelse(SlicerMorph.repc$UKC == "NATURAL" | 
                                             SlicerMorph.repc$UKC == "FOX", 
                                           "Natural", "Domesticated"), 
                                    ordered = T, levels = c("Domesticated", "Natural"))

## Calculating group means for setting skulls on plots: 
UKC <- SlicerMorph.repc[!is.na(SlicerMorph.repc$UKC),]
UKC <- UKC[,c("PC.1", "PC.2", "UKC")]
UKC_scenthound_mean <- data.frame(x = mean(UKC$PC.1[UKC$UKC == "Scenthound"]),
                           y = mean(UKC$PC.2[UKC$UKC == "Scenthound"]))
UKC_companion_mean <- data.frame(x = mean(UKC$PC.1[UKC$UKC == "Companion"]),
                                 y = mean(UKC$PC.2[UKC$UKC == "Companion"]))
AKC <- SlicerMorph.repc[!is.na(SlicerMorph.repc$AKC),]
AKC <- AKC[,c("PC.1", "PC.2", "AKC")]
AKC_hound_mean <- data.frame(x = mean(AKC$PC.1[AKC$AKC == "Hound"]),
                             y = mean(AKC$PC.2[AKC$AKC == "Hound"]))
AKC_nonsporting_mean <- data.frame(x = mean(AKC$PC.1[AKC$AKC == "Non-sporting"]),
                             y = mean(AKC$PC.2[AKC$AKC == "Non-sporting"]))
AKC_toy_mean <- data.frame(x = mean(AKC$PC.1[AKC$AKC == "Toy"]),
                             y = mean(AKC$PC.2[AKC$AKC == "Toy"]))
```

K-means clustering plot: 

```{r k-means, echo=FALSE, warning=FALSE, fig.cap="K-means clustering plot."}
#now build the plot
pkmean <- ggplot(SlicerMorph.repc[!is.na(SlicerMorph.repc$UKC),], aes(PC.1, PC.2, color = cluster, 
                                   fill = cluster,
                                   shape = domestic)) + 
  geom_point(size = 2) +
  stat_ellipse(geom = "polygon", aes(fill = cluster, group = cluster), level = 0.95, alpha = 0.1) +
  scale_shape_manual(values = c(5, 19), name = " ") +
  scale_color_munch(choice = "Nietzsche", discrete = TRUE,name = "Cluster") + 
  scale_fill_munch(choice = "Nietzsche", discrete = TRUE, name = "Cluster") +
  geom_text_repel(label = SlicerMorph.repc$Breed[!is.na(SlicerMorph.repc$UKC)], max.overlaps = 4, show.legend = FALSE)+
  xlab("PC 1 (Var = 50.2%)") + ylab("PC 2 (Var = 13.3%)") +
  theme_bw()
pkmean
```

Kennel-club groupings and PC results:

```{r kc-groups, echo=FALSE, warning=FALSE, fig.cap="K-means clustering plot.", fig.height=5.5, fig.width=12}
#now build the plot
kc_palette <- munch_palette("Murderer", 8)
kc_palette <- c(kc_palette, kc_palette[2])
pUKC <- ggplot(SlicerMorph.repc[!is.na(SlicerMorph.repc$UKC),], aes(PC.1, PC.2, color = UKC.shapefact, 
                                   fill = UKC.shapefact,
                                   shape = UKC.shapefact)) + 
  geom_point(size = 2) +
  stat_ellipse(geom = "polygon", aes(group = UKC.shapefact), level = 0.95, alpha = 0.2) +
  scale_shape_manual(values = c(19, 0, 1, 2, 5, 6, 7, 9, 10), name = "UKC Groups") +
  scale_color_manual(values = kc_palette, name = "UKC Groups") + 
  scale_fill_manual(values = kc_palette, name = "UKC Groups") +
  geom_text_repel(label = SlicerMorph.repc$Breed[!is.na(SlicerMorph.repc$UKC)], 
                  max.overlaps = 4, show.legend = FALSE) +
  annotate("segment", x = 0.1, xend = UKC_companion_mean$x, y = -0.1, yend = UKC_companion_mean$y,
             lty = 1, color = kc_palette[4]) +
  annotate("segment", x = -0.24, xend = UKC_scenthound_mean$x, y = -0.1, yend = UKC_scenthound_mean$y,
             lty = 1, color = kc_palette[9]) +
  xlim(-0.31, NA) +
  ylim(-0.20, NA) +
  xlab("PC 1 (Var = 50.2%)") + ylab("PC 2 (Var = 13.3%)") +
  theme_bw()
pAKC <- ggplot(SlicerMorph.repc[!is.na(SlicerMorph.repc$AKC),], aes(PC.1, PC.2, color = AKC.shapefact, 
                                   fill = AKC.shapefact,
                                   shape = AKC.shapefact)) + 
  geom_point(size = 2) +
  stat_ellipse(geom = "polygon", aes(fill = AKC.shapefact, 
                                     group = AKC.shapefact), level = 0.95, alpha = 0.2) +
  scale_shape_manual(values = c(19, 0, 1, 2, 5, 6, 7, 9, 10), name = "AKC Groups") +
  scale_color_munch(choice = "Murderer", discrete = TRUE, name = "AKC Groups") + 
  scale_fill_munch(choice = "Murderer", discrete = TRUE, name = "AKC Groups") +
  geom_text_repel(label = SlicerMorph.repc$Breed[!is.na(SlicerMorph.repc$AKC)], max.overlaps = 4, show.legend = FALSE)+
  annotate("segment", x = -0.22, xend = AKC_hound_mean$x, y = -0.05, yend = AKC_hound_mean$y,
             lty = 1, color = kc_palette[8]) +
  annotate("segment", x = 0.1, xend = AKC_nonsporting_mean$x, y = 0.15, yend = AKC_nonsporting_mean$y,
             lty = 1, color = kc_palette[7]) +
  annotate("segment", x = 0.16, xend = AKC_toy_mean$x, y = -0.07, yend = AKC_toy_mean$y,
             lty = 1, color = kc_palette[4]) +
  xlim(-0.31, NA) +
  ylim(NA, 0.20) +
  xlab("PC 1 (Var = 50.2%)") + ylab("PC 2 (Var = 13.3%)") +
  theme_bw() 

pUKC + pAKC
```


## Discriminant Factors between groups

Comparison of landmarks between group means

```{r}
# Extracting first 5 PCs and each kennel-club grouping scheme
skulls <- cbind(SlicerMorph.repc[, 1:5], 
                "UKC" = factor(SlicerMorph.repc$UKC, ordered = T, levels = UKC_group_order), 
                "AKC" = factor(SlicerMorph.repc$AKC, ordered = T, levels = AKC_group_order),
                "bitework" = factor(SlicerMorph.repc$Bitework),
                "scentwork" = factor(SlicerMorph.repc$Nosework))
skulls <- na.omit(skulls)

# Create a paired and ordered GPA-grouping object
mixdrop<-gpa$coords[,,order(dimnames(gpa$coords)[[3]])]
mixdrop<-mixdrop[,,-96]

reforge<-geomorph.data.frame(shape = mixdrop,
                             UKC = na.omit(factor(SlicerMorph.repc$UKC, ordered = T)),
                             AKC = na.omit(factor(SlicerMorph.repc$AKC, ordered = T)),
                             bitework = na.omit(factor(SlicerMorph.repc$Bitework[-96])),
                             scentwork = na.omit(factor(SlicerMorph.repc$Nosework[-96])))

# Create a function that subsets the mean landmarks for each group and does a pairwise subtraction and squaring of those means
meanforge <- function(target, group) {
  #sub-setting the means and sending them to a new object
  new.coords<-coords.subset(target[["shape"]], group = target[[group]])
  output_means<-lapply(new.coords, mshape)
  #perform the subtraction and squaring of the pairwise combinations
  result<- combn(output_means, 2, function(x) (x[[1]]-x[[2]])^2, simplify = FALSE)
  #rename the pairs so they're readable
  names(result)<-combn(names(output_means), 2, function(n) paste(n[1], "-", n[2]), simplify = TRUE)
  return(result)
}

#now run the function for each grouping scheme. Each member of the list will be named based on which two groups are compared
meanshape.UKC<-meanforge(reforge,"UKC")
meanshape.AKC<-meanforge(reforge,"AKC")
meanshape.nose<-meanforge(reforge,"scentwork")
meanshape.bite<-meanforge(reforge,"bitework")

```

Reformatting data for visualization:

```{r}
calc_gpa_dist <- function(dat){
  require(tidyr)
  n <- length(names(dat))
  results <- matrix(NA, nrow = nrow(dat[[1]]), ncol = n)
  results <- as.data.frame(results)
  results <- cbind(seq(1,nrow(results)), results)
  colnames(results) <- c("landmark",names(dat))
  
  for(i in names(dat)){
    results[[i]] <- sqrt(dat[[i]][,"X"]^2 + dat[[i]][,"Y"]^2 + dat[[i]][,"Z"]^2)
  }
  results_long <- pivot_longer(results, cols = -"landmark", names_to = "comparison")
  results_long$landmark <- factor(results_long$landmark)
  return(results_long)
}

meanshape.AKC[["plotting"]] <- calc_gpa_dist(meanshape.AKC)
meanshape.UKC[["plotting"]] <- calc_gpa_dist(meanshape.UKC)
meanshape.bite[["plotting"]] <- calc_gpa_dist(meanshape.bite)
meanshape.bite[["cutoff"]] <- data.frame("mean" = mean(meanshape.bite[["plotting"]]$value), 
                                        "sd" = sd(meanshape.bite[["plotting"]]$value))
meanshape.nose[["plotting"]] <- calc_gpa_dist(meanshape.nose)
meanshape.nose[["cutoff"]] <- data.frame("mean" = mean(meanshape.nose[["plotting"]]$value), 
                                        "sd" = sd(meanshape.nose[["plotting"]]$value))

meanshape.AKC[["plotting"]]$short <- ifelse(grepl("toy", meanshape.AKC[["plotting"]]$comparison, 
                                                  ignore.case = T),
                                           "toy", 
                                           ifelse(grepl("natural", meanshape.AKC[["plotting"]]$comparison,
                                                        ignore.case = T),
                                                  "natural", "other"))
meanshape.AKC[["plotting"]]$short <- factor(meanshape.AKC[["plotting"]]$short, 
                                            ordered = T, levels = c("toy", "natural", "other"))
meanshape.AKC[["cutoff"]] <- data.frame("mean" = mean(meanshape.AKC[["plotting"]]$value), 
                                        "sd" = sd(meanshape.AKC[["plotting"]]$value))
meanshape.UKC[["plotting"]]$short <- ifelse(grepl("companion", meanshape.UKC[["plotting"]]$comparison,
                                                      ignore.case = T),
                                           "companion", 
                                           ifelse(grepl("natural", meanshape.UKC[["plotting"]]$comparison,
                                                      ignore.case = T),
                                           "natural", "other"))
meanshape.UKC[["plotting"]]$short <- factor(meanshape.UKC[["plotting"]]$short, 
                                            ordered = T, levels = c("companion", "natural", "other"))
meanshape.UKC[["cutoff"]] <- data.frame("mean" = mean(meanshape.UKC[["plotting"]]$value), 
                                        "sd" = sd(meanshape.UKC[["plotting"]]$value))

```

AKC & UKC groups:
```{r}
p_UKC_compare <- ggplot(meanshape.UKC[["plotting"]], aes(landmark, value, color = short)) + 
  geom_point(position = position_jitter(width = 0.1)) + 
  geom_hline(yintercept = meanshape.UKC[["cutoff"]]$mean+2*meanshape.UKC[["cutoff"]]$sd,
             lty = 2) +
  scale_color_viridis_d(name = "Comparison\nagainst:") +
  annotate("segment", x = 1, xend = 20, y = 0.0085, yend = 0.0085,
             lty = 1, color = "red") +
  annotate("text", x = 10, y = 0.0075, label = "Dorsal features", color = "red") +
  annotate("segment", x = 21, xend = 38, y = 0.0085, yend = 0.0085,
             lty = 1, color = "blue") +
  annotate("text", x = 30, y = 0.0075, label = "Ventral features", color = "blue") +
  ylab("Group-mean landmark\ndistance squared") +
  ggtitle("UKC group comparisons") +
  theme_bw() #+
  #theme(legend.position = "bottom")
  #theme(legend.position = "inside", legend.position.inside = c(0.75,0.65),
  #      legend.background = element_rect(color = "gray30", fill = "white", linetype="solid",
  #                                       linewidth = 0.2))

p_AKC_compare <- ggplot(meanshape.AKC[["plotting"]], aes(landmark, value, color = short)) + 
  geom_point(position = position_jitter(width = 0.1)) + 
  geom_hline(yintercept = meanshape.AKC[["cutoff"]]$mean+2*meanshape.AKC[["cutoff"]]$sd,
             lty = 2) +
  scale_color_viridis_d(name = "Comparison\nagainst:") +
  annotate("segment", x = 1, xend = 20, y = 0.0085, yend = 0.0085,
             lty = 1, color = "red") +
  annotate("text", x = 10, y = 0.0075, label = "Dorsal features", color = "red") +
  annotate("segment", x = 21, xend = 38, y = 0.0085, yend = 0.0085,
             lty = 1, color = "blue") +
  annotate("text", x = 30, y = 0.0075, label = "Ventral features", color = "blue") +
  ylab("Group-mean landmark\ndistance squared") +
  ggtitle("AKC group comparisons") +
  theme_bw() #+
  #theme(legend.position = "bottom")
    #theme(legend.position = "inside", legend.position.inside = c(0.75,0.65),
    #    legend.background = element_rect(color = "gray30", fill = "white", linetype="solid",
    #                                     linewidth = 0.2))
p_AKC_compare / p_UKC_compare
```
Nosework and Bitework: 

```{r}
p_bite_compare <-  ggplot(meanshape.bite[["plotting"]], aes(landmark, value, color = comparison)) + 
  geom_point(position = position_jitter(width = 0.1)) + 
  geom_hline(yintercept = meanshape.bite[["cutoff"]]$mean+2*meanshape.bite[["cutoff"]]$sd,
             lty = 2) +
  annotate("segment", x = 1, xend = 20, y = 0.007, yend = 0.007,
             lty = 1, color = "red") +
  annotate("text", x = 10, y = 0.0065, label = "Dorsal features", color = "red") +
    annotate("segment", x = 21, xend = 38, y = 0.007, yend = 0.007,
             lty = 1, color = "blue") +
  annotate("text", x = 30, y = 0.0065, label = "Ventral features", color = "blue") +
  scale_color_viridis_d(name = "Group-group\ncomparison") +
  ylab("Group-mean landmark\ndistance squared") +
  ggtitle("Bite-work group comparisons") +
  theme_bw()
p_nose_compare <- ggplot(meanshape.nose[["plotting"]], aes(landmark, value, color = comparison)) + 
  geom_point(position = position_jitter(width = 0.1)) + 
  geom_hline(yintercept = meanshape.nose[["cutoff"]]$mean+2*meanshape.nose[["cutoff"]]$sd,
             lty = 2) +
  annotate("segment", x = 1, xend = 20, y = 0.007, yend = 0.007,
             lty = 1, color = "red") +
  annotate("text", x = 10, y = 0.0065, label = "Dorsal features", color = "red") +
    annotate("segment", x = 21, xend = 38, y = 0.007, yend = 0.007,
             lty = 1, color = "blue") +
  annotate("text", x = 30, y = 0.0065, label = "Ventral features", color = "blue") +
  scale_color_viridis_d(name = "Group-group\ncomparison") +
  ylab("Group-mean landmark\ndistance squared") +
  ggtitle("Scent-work group comparisons") +
  theme_bw()

p_bite_compare / p_nose_compare + plot_layout(guides = "collect")
```

## MANOVA 

```{r}
man_UKC <- manova(as.matrix(skulls[,1:5])~skulls$UKC)
summary.aov(man_UKC, test="Pillai")

#using Geomorph and doing pairwise comparisons
manUKC<-procD.lm(shape ~ UKC, data = reforge, RRPP = TRUE)
manAKC<-procD.lm(shape ~ AKC, data = reforge, RRPP = TRUE)
manBite<-procD.lm(shape ~ bitework, data = reforge, RRPP = TRUE)
manScent<-procD.lm(shape ~ scentwork, data = reforge, RRPP = TRUE)

hold.ukc<-manova(manUKC)
hold.akc<-manova(manAKC)
hold.bite<-manova(manBite)
hold.nose<-manova(manScent)

ukcpairs<-pairwise(manUKC, groups = reforge$UKC)
akcpairs<-pairwise(manAKC, groups = reforge$AKC)
bitepairs<-pairwise(manBite, groups = reforge$bitework)
nosepairs<-pairwise(manScent, groups = reforge$scentwork)


pvals.UKC<-summary(ukcpairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.UKC<-pvals.UKC$pairwise.tables$P[lower.tri(pvals.UKC$pairwise.tables$P)]
pvals.AKC<-summary(akcpairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.AKC<-pvals.AKC$pairwise.tables$P[lower.tri(pvals.AKC$pairwise.tables$P)]
pvals.bite<-summary(bitepairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.bite<-pvals.bite$pairwise.tables$P[lower.tri(pvals.bite$pairwise.tables$P)]
pvals.nose<-summary(nosepairs, test.type= "dist", confidence = 0.95, stat.table = TRUE)
pvals.nose<-pvals.nose$pairwise.tables$P[lower.tri(pvals.nose$pairwise.tables$P)]

pvals.ukc.adjusted<-p.adjust(pvals.UKC, method = "bonf")
pvals.akc.adjusted<-p.adjust(pvals.AKC, method = "bonf")
pvals.bite.adjusted<-p.adjust(pvals.bite, method = "bonf")
pvals.nose.adjusted<-p.adjust(pvals.nose, method = "bonf")
```

```{r}
lda_UKC <- MASS::lda(skulls$UKC~as.matrix(skulls[,1:5]))
lda_UKC
```

## Testing of Functional Groups

```{r fxn-groupings, echo=FALSE, message=FALSE, warning=FALSE, fig.height=3.5, fig.cap="Bitework and scent work."}
fxn_group_palette <- munch_palette("Murderer",8)[c(1:3,5)]
pbite <- ggplot(SlicerMorph.repc, aes(PC.1, PC.2, color = Bitework, 
                                   fill = Bitework,
                                   shape = Bitework)) + 
  geom_point() +
  stat_ellipse(geom = "polygon", level = 0.95, alpha = 0.1) +
  scale_shape_manual(values = c(21, 23, 0, 2), name = " ") +
  scale_color_manual(values = fxn_group_palette, name = " ") + 
  scale_fill_manual(values = fxn_group_palette, name = " ") +
  xlab("PC 1 (Var = 50.2%)") + 
  ylab(" ") +
  ggtitle("Selected for bite work") +
  theme_bw() + theme(legend.position = "right")
pscent <- ggplot(SlicerMorph.repc, aes(PC.1, PC.2, color = Nosework, 
                                   fill = Nosework,
                                   shape = Nosework)) + 
  geom_point() +
  stat_ellipse(geom = "polygon", level = 0.95, alpha = 0.1) +
  scale_shape_manual(values = c(21, 23, 0, 2), name = " ") +
  scale_color_manual(values = fxn_group_palette, name = " ") + 
  scale_fill_manual(values = fxn_group_palette, name = " ") +
  xlab("PC 1 (Var = 50.2%)") + 
  ylab("PC 2 (Var = 13.3%)") +
  ggtitle("Selected for scent work") +
  theme_bw() 
pscent + pbite + plot_layout(guides = "collect") & theme(legend.position = "right")

```

```{r bite-force, echo=FALSE, message=FALSE, warning=FALSE}
bite_data <- data.frame("Breed" = SlicerMorph.repc$Breed, 
                        "Bitework" = SlicerMorph.repc$Bitework, 
                        "Domestic" = SlicerMorph.repc$domestic, 
                        "UKC" = SlicerMorph.repc$UKC, 
                        "AKC" = SlicerMorph.repc$AKC,
                        "canine" = SlicerMorph.repc$bfq_canine, 
                        "carnassial" = SlicerMorph.repc$bfq_carnassial)
bite_data_long <- pivot_longer(bite_data, cols = c("canine","carnassial"))
pforce <- ggplot(bite_data_long, aes(Bitework, value, fill = name)) + 
  geom_boxplot(alpha = 0.6, outlier.shape = NA) + 
  geom_point(alpha = 0.5, position = position_jitterdodge(jitter.width = 0.2), shape = 21) +
  scale_fill_manual(values = munch_palette("YellowLog",2), name = " ") +
  ylab("Bite-force quotient") + xlab("Selected for bite work") +
  theme_bw()
pforce
```

```{r bite-force-stats}
bite_yes_canine <- bite_data$canine[bite_data$Bitework == "yes"]
bite_no_canine <- bite_data$canine[bite_data$Bitework == "no"]
shapiro.test(bite_yes_canine)
shapiro.test(bite_no_canine)
t.test(bite_yes_canine, bite_no_canine)

bite_yes_carnassial <- bite_data$carnassial[bite_data$Bitework == "yes"]
bite_no_carnassial <- bite_data$carnassial[bite_data$Bitework == "no"]
shapiro.test(bite_yes_carnassial)
shapiro.test(bite_no_carnassial)
t.test(bite_yes_carnassial, bite_no_carnassial)

bite_natural_canine <- bite_data$canine[bite_data$Bitework == "NATURAL"]
shapiro.test(bite_natural_canine)
bite_fox_canine <- bite_data$canine[bite_data$Bitework == "FOX"]
shapiro.test(bite_fox_canine)

t.test(bite_yes_canine, bite_natural_canine)
t.test(bite_yes_canine, bite_fox_canine)
```

## For the paper: 

Figure 2: k-means cluster on PC plot

```{r fig-2, echo=FALSE, warning=FALSE, fig.cap="A. K-means clustering plot.", fig.height=4, fig.width=9.6}
pkmean + xlim(-0.22, 0.25) + theme(legend.margin=margin(c(0,0,0,0)))
ggsave("./results/figures/figure2.pdf", height = 4, width = 9.6)
```

Figure 3: UKC comparison figure

```{r fig-3, echo=FALSE, warning=FALSE, fig.cap="A. K-means clustering plot. B. AKC groupings. C. UKC groupings.", fig.height=7.5, fig.width=12}
design2 <- 
  "AAAA
   AAAA
   BBBB"
(pUKC ) / 
  free(p_UKC_compare + ggtitle(" ") +  
         theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))) + 
  plot_annotation(tag_levels = "A") +
  plot_layout(design = design2) & theme(legend.margin=margin(c(0,0,0,0)))
  
ggsave("./results/figures/figure3.pdf", height = 3.45*2, width = 3.25*2)
```

Figure 4: AKC comparison figure

```{r fig-4, echo=FALSE, warning=FALSE, fig.cap=" AKC groupings. ", fig.height=7.5, fig.width=12}
design2 <- 
  "AAAA
   AAAA
   BBBB"
pAKC / free(p_AKC_compare + ggtitle(" ") + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)))+
  plot_annotation(tag_levels = "A") +
  plot_layout(design = design2) & theme(legend.margin=margin(c(0,0,0,0)))
ggsave("./results/figures/figure4.pdf", height = 3.45*2, width = 3.25*2)
```

Figure 5: task-specific group results

```{r fig-5, echo=FALSE, warning=FALSE, fig.cap="A. K-means clustering plot. B. AKC groupings. C. UKC groupings.", fig.height=7.5, fig.width=12}
design2 <- 
  "AAAA
   BBBB
   BBBB
   CCCC"
(pscent + pbite + plot_layout(guides = "collect") & theme(legend.position = "right")) / 
  (p_nose_compare /p_bite_compare + 
     plot_layout(guides = "collect") & theme(legend.position = "right", 
                                             axis.text.x = element_text(angle = 90, 
                                                                        vjust = 0.5, hjust=1))) / 
  pforce  + 
  plot_annotation(tag_levels = "A") +
  plot_layout(design = design2) & theme(legend.margin=margin(c(0,0,0,0)))
ggsave("./results/figures/figure5.pdf", width = 9.6, height = 11.52)
```






